Quantitative Data Analysis¶

With the relational information we are now able to corellate resources and joined topologies from varius information sources. This gives you the real power, while having the underlying relational structure, you can gather unstructured metrics, events, alarms and put them into proper context in you managed resources.

The metrics collected from you infrastrucute by means of local monitorin system can be assigned to various vertices and edges in your network. This can give you more insight to the utilisation of depicted infrastructures.

Query Options¶

Time-series Metrics¶

Parameters that apply only for the range metrics.

start: Time range start.
end: Time range end.
step: Query resolution step width.

Instant Metric¶

Parameters that apply only for the intant meters.

moment: Single moment in time.

Alarm Options¶

Following lists show allowed values for alarm functions, the alarm arithmetic operators and aggregation function for range meters.

Supported Time-series Aggregations¶

avg: Arithmetic average of the series values.
min: Use the minimal value from series.
max: Use the maximal value from series.
sum: Sum the values together.

Advanced Usage¶

You can have the following query to the prometheus server that gives you the rate of error response codes goint through a HAproxy for example.

sum(irate(haproxy_http_response_5xx{
    proxy=~"glance.*",
    sv="FRONTEND"
}[5m]))

Or you can have the query with the same result to the InfluxDB server:

SELECT sum("count")
    FROM "openstack_glance_http_response_times"
    WHERE "hostname" =~ /$server/
        AND "http_status" = '5xx'
        AND $timeFilter
    GROUP BY time($interval)
fill(0)

Having these metrics you can assign numerical properties of your relational nodes with these values and use them in correct context.